Breath-based sensors for non-invasive molecular detection

ABSTRACT

A method of diagnosing the health of an individual by collecting a breath sample from the individual and measuring the amount of each of a plurality of analytes in the sample. The amount of each analytes is measured by fitting a time response curve of a sample-evaluation fuel cell in which the fuel cell sample electrode is contacted with the sample with the analysis based on a function of standard time response curves for an equivalent fuel cell configuration obtained separately for each of the analytes on a fuel cell with equivalent construction as sample-evaluation fuel cell. Each of the plurality of analytes is generally indicative of an aspect of the individual&#39;s health. Suitable analytes include, for example, inorganic compounds as well as compositions that exhibit negative reduction reactions at least for a portion of the time response curve. In particular, acetone exhibits a negative potential/current peak when it is an analyte in a fuel cell in an sample electrode with a counter electrode exposed to oxygen, which may or may not be introduced in the form of air. Various forms of analysis to estimate acetone concentrations in the breath can be used.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a division of U.S. application Ser. No. 11/305,799filed Dec. 16, 2005, which claims priority to U.S. provisionalapplication 60/636,951 filed on Dec. 17, 2004 to Leddy et al., bothincorporated herein by reference in their entireties.

FIELD OF THE INVENTION

The present invention relates to approaches for measuring anddifferentiating volatile organic and/or inorganic compositions in vaporsamples. More particularly, the present invention relates to using fuelcells as sensors for measuring the amount of various diagnosticanalytes, which can be characteristic of a disease state, in anindividual's breath.

BACKGROUND OF THE INVENTION

Acetone found in a human's blood, urine, and breath can be a marker forvarious biological processes, the most notable of which is diabeticketoacidosis associated with insulin insufficiency. Also, acetone canalso be an indicator of poor regulation of a ketogenic diet that is usedto control refractory epileptic seizures.

Conventional monitoring devices for diabetic ketoacidosis and regulatingketogenic diets often rely on invasive sample collection, such as bloodtests. The American Diabetes Association recommends that diabeticsmonitor their glucose levels several times a day. However, because ofthe invasive nature of conventional monitoring devices, many diabeticswith type 1 (“insulin dependent”) diabetes monitor glucose levels onlyonce a day and most diabetics with type 2 (“insulin resistant”) diabetesno not monitor glucose levels daily.

Ketone generation in the body is known to be associated with certainconditions. Referring to FIG. 1, insulin facilitates the transport ofglucose into the cell to generate energy. Diabetes generally occurs wheneither the amount of insulin is insufficient (type 1 diabetes) or theinsulin is not effective (type 2 diabetes). As a result, the bloodglucose level can rise and the cells become glucose-starved. Ketogenesisin the mitochondria then converts triglycerides (fatty acids) toacetoacetate (AcAc) and energy. The AcAc interconverts with3-hydroxybutyrate (3HB) and also undergoes spontaneous decarboxylationto form acetone (Me₂O). Together these three products (AcAc, 3HB, andMe₂O) are known as ketone bodies, which can partition across the cellwall and into the blood.

Of the three ketone bodies, only acetone is sufficiently volatile topartition into the alveolar air, while AcAc and 3HB remain in the blood.The partition coefficient, K for Me₂O at the blood/air interface isbetween 208 and 597, a factor even more favorable that that of ethanol.The ethanol partition coefficient is used in determining the bloodalcohol content or blood alcohol concentration (BAC) of an individual.The acetone that partitions in to the alveolar air generates the sweetsmell characteristic of diabetic ketoacidosis, which is sometimesreferred to as “acetone breath.”

Diabetic ketoacidosis occurs as the fatty acids are consumed and theconcentration of ketone bodies rises. For normal subjects, theconcentration ratio of 3HB to AcAc is about 1:1 and the totalconcentration of ketone bodies is below 0.5 mM. Under diabeticketoacidotic conditions, the ratio of 3HB to AcAc increases to about3:1, or even as high as 10:1, and the concentration of the ketone bodiesdrastically increases. Concentrations for the ketone bodies are listedin Table 1 for human subjects who are healthy individuals, treateddiabetics, and ketoacidotic diabetics.

TABLE 1 Plasma Concentrations of Ketone Bodies in Plasma (mM).Ketoacidotic Ketone Body Normal Subject Treated Diabetic DiabeticAcetone (Me₂O) 0.015 ± 0.005 1.69 ± 0.78 3.26 ± 0.79 Acetoacetate (AcAc)0.114 ± 0.029 0.306 ± 0.05  2.84 ± 0.40 3-Hydroxy-butyrate 0.160 ± 0.0500.810 ± 0.171 8.23 ± 1.48 (3KB) pH — — 7.29 ± 0.01

As can be seen in Table 1, the concentration of acetone in aketoacidotic diabetic is approximately two times greater than that of atreated diabetic, and the concentration of acetone is roughly a hundredtimes greater in a treated diabetic than a normal subject. Also, theconcentrations of AcAc and 3HB in a ketoacidotic diabetic are roughly 25times higher and 50 times higher than that of a normal subject,respectively.

The ketone body composition illustrates that acetone measurement can bean effective marker for the onset of ketoacidosis and the ketoacidoticstate. Ketoacidosis can be followed by the 3HB concentration as ittracks with the total ketone load. Breath acetone correlates with plasma3HB over a clinically relevant range. Thus, by tracking acetone on thebreath, 3HB can be reliably measured and the onset of ketoacidosis canbe tracked.

Portable sensors have been developed for measuring alcohol on a human'sbreath. Breathalyzers determine the BAC by measuring the ethanolconcentration in alveolar air that is exhaled from deep within thelungs. Because there is an equilibrium of ethanol between the blood andalveolar air, the ethanol concentration in the breath is generallyproportional to the ethanol concentration in the blood.

SUMMARY OF THE INVENTION

In all of the following, the potential/current time response curve isdiscussed with respect to a sample analyte being exposed to a fuel cellsample electrode and a selected reactant exposed to the counterelectrode. In principle, the electronics for making the measurement canbe connected with either polarity, although only one connection resultsin the conventional response curve. To simplify the discussion, thefollowing is based on a conventional connection for measuring theresponse curve with a positive displacement corresponding to oxidationat the sample electrode and reduction at the counter electrode. If thealternative connection is made, all of the values can be reversed insign to use the analysis below, although alternatively the sign can bechanged and notations correspondingly flipped in the following analyses.

In a first aspect, the invention pertains to a method for the estimationof acetone concentration in a person's breath. Generally, the methodcomprises fitting a time response curve of a sample-evaluation fuel cellin which a sample electrode of the fuel cell is exposed to a breathsample and a counter electrode of the fuel cell is exposed to O₂. Thefitting can be performed through the de-convolution of the sample timeresponse curve in comparison of the sample time response curve with thetime response curve of standard aqueous acetone solutions. This methodcan be adapted for the evaluation of diabetic ketoacidosis of anindividual by estimating the concentration of acetone in a person'sblood stream from an estimate of acetone concentration within the breathsample.

In further aspects, the invention pertains to a method for diagnosingthe health of an individual in which the method comprises measuring theamount of a plurality of analytes in a breath sample from an individual.The measurements are obtained through fitting a time response curve of asample-evaluation fuel cell. In some embodiments, at least one of theanalytes has a negative potential peak in the time response curveindicating a reduction process. A sample electrode of the fuel cell isexposed to a breath sample, and the counter electrode of the fuel cellis exposed to a selected reactant. The selected reactant can compriseO₂, which may or may not be delivered as air.

In other aspects, the invention pertains to a method for diagnosing thehealth of an individual in which the method comprises measuring theamount of a plurality of analytes in a breath sample from an individual.The measurements are obtained through fitting a time response curve of asample-evaluation fuel cell. In some embodiments, at least one of theanalytes is an inorganic compound. A sample electrode of the fuel cellis exposed to a breath sample, and the counter electrode of the fuelcell is exposed to a selected reactant. The selected reactant cancomprise O₂, which may or may not be delivered as air.

Moreover, the invention pertains to a system comprising a flowapparatus, a fuel cell and an analyzer. The flow apparatus is configuredto operably receive a breath sample from an individual. The fuel cellcomprises a sample electrode operably coupled to the flow apparatus anda counter electrode exposed to O₂. The sample transported within theflow system operably contacts the sample electrode. The analyzerreceives a signal related to the potential of the fuel cell andevaluates the amount of acetone from a time response of the fuel cellsignal as a function of time based on a standard time response of thefuel cell with acetone.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic representation of fatty acid metabolism.

FIG. 2 is a schematic representation of a system for evaluation ofamounts of volatile organic compositions in a vapor sample as describedherein.

FIG. 3 is a plot of fuel cell response as a function of time for threefuel cells using a standard ethanol sample to compare the results ofdifferent fuel cells assembled into the apparatus for evaluating vaporsamples.

FIG. 4 is a plot of fuel cell response as a function of time for breathsamples taken from a person after taking a drag on a cigarette, aftertaking one clean breath after a drag on a cigarette, and after two cleanbreaths following a drag on a cigarette.

FIG. 5 is a plot of fuel cell response for the breath of two non-smokersand one smoker with at least 24 hours since having smoked their lastcigarette.

FIG. 6 is a plot of fuel cell as a function of time after an unknownsample from a smoker with ethanol on their breath, plotted along with afit to ethanol and smoke from standard curves and the resulting curvefit to the unknown sample.

FIG. 7 is a plot of the absolute value of the unknown sample responseminus the normalized recombination fit for the data in FIG. 6.

FIG. 8 is a plot of fuel cell as a function of time after a secondunknown sample from a smoker with ethanol on their breath, plotted alongwith a fit to ethanol and smoke from standard curves and the resultingcurve fit to the second unknown sample.

FIG. 9 is a plot of the absolute value of the second unknown sampleresponse minus the normalized recombination fit for the data in FIG. 8.

FIG. 10 is a plot of fuel cell response as a function of time for a“clean” breath sample from a healthy individual who had eaten roughlytwo hours prior to providing the breath samples.

FIG. 11 is a plot of fuel cell response as a function of time for a fuelcell using a 27 mM standard acetone sample.

FIG. 12 is a plot of fuel cell response as a function of time for a fuelcell using a 140 mM standard acetone sample.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

A vapor analysis system comprising a breath-based sensor can be used fornoninvasive detection of acetone and other analytes in a human's breath.The analysis of these analytes in the breath can be used for diagnosticpurposes and/or for the evaluation of a particular condition to whichthe person is known to be susceptible. For example, acetone is a markerfor various biological processes, the most notable of which is diabeticketoacidosis that is associated with insulin insufficiency and poorregulation of the ketogenic diet used to control refractory epilepticseizures. The breath-based sensor, which can be portable, according tothe various embodiments provides a highly selective, non-invasive, andrapid measurement device having the accuracy, precision, and detectionlimit appropriate to the assay of acetone in the physiologicallyrelevant range for these conditions. Other compounds characteristic ofvarious disease states can also be measured by the breath-based sensoras described herein. Representative compounds include, for example,carbonyl sulfide (COS) as a marker for lung transplant rejection,ammonia (NH₃) in end stage renal failure, carbon disulfide (CS₂) as amarker for coronary artery disease, alkanes and benzene derivatives inthe breath of lung cancer patients, and nitric oxide (NO) as a markerfor asthma. Thus, the techniques herein extend to volatile inorganicanalytes in a person's breath. Also, the sample evaluations are shown toextend to analytes, such as acetone, that undergo a reduction reactionat the sample electrode, at least over a portion of the time responsecurve, against a counter electrode exposed to a suitable reactant, suchas oxygen, i.e., O₂.

The signature of the time response curve for a fuel cell signal, e.g.,voltage/potential or current, upon exposure with volatile organiccompositions from a breath sample can be used to evaluate the relativeconcentrations of a combination of compositions in the sample.Corresponding systems can be designed to collect a vapor sample thatcontains volatile compositions and direct the sample to the sampleelectrode of a fuel cell. The fuel cell response as a function of timeis a signature for a particular compound. For samples that have aplurality of volatile organic compositions, the fuel cell response isfor practical purposes a linear or nonlinear combination of the responseof the fuel cell for the particular compositions appropriately weighedfor the relative amounts. Therefore, an analysis can be performed tode-convolute the time response curve to obtain values for the relativeconcentrations. Average standard curves can be used for the performanceof the de-convolution. For samples such as acetone with a distinctivenegative reduction peak in the time response curve, fitting of the area,the depth of the peak or the general time response curve shape can beused to estimate concentrations of acetone or other reducing analytes.In some embodiments, the vapor sample is a breath sample from a person,or other patient, such as a pet or farm animal for medical evaluation.

Generally, a volatile organic composition evaluation system comprises avapor/gas sampling component, a flow apparatus, a fuel cell and ananalysis instrument. Vapor refers generally to gas(es) and/or vapor of avolatile composition(s) at a particular vapor pressure. The samplingcomponent comprises an appropriate collection system suitable for theparticular application of the system. For a breath analyzer, thesampling component can comprise a mouthpiece and associated conduits toconnect the flow with the flow apparatus. The flow apparatus directs thegas sample to a fuel cell. The fuel cell comprises a sample electrodeoperably connected to the flow apparatus in which the fuel cellgenerates a voltage or current in response to a broad range of volatileorganic and/or inorganic compositions reacting at the sample electrode.The counter electrode can be exposed to a selected reactant. In someembodiments, the counter electrode can be exposed to air, although analternative oxygen (O₂) source or other chemical supply can be used tosupply reactant to the counter electrode. The analysis instrumentmeasures the fuel cell output signal, e.g., voltage or current, as afunction of time from the fuel cell and evaluates the composition of thevapor in response to the fuel cell performance as a function of time.

The sample electrode can function as an anode for the oxidation of theanalyte while the electrode exposed to atmospheric oxygen functions as acathode to reduce the molecular oxygen (O₂). However, alternatively, thesample electrode can function as a cathode with the reduction of theanalyte and with the corresponding oxidation taking place at theelectrode exposed to air or other reactant. In addition, the respectiveelectrodes can function partially as both anodes and cathodes eithersimultaneously or sequentially with the passage of time as differentanalytes react within the analyzer. For example, as discussed below,acetone is reduced in the analyzer at least initially although theresulting reduced compound may be subsequently oxidized. Also, oneanalyte such as acetone may be reduced while another analyte, such asethanol, may be oxidized.

Fuel cells of particular interest are proton exchange membrane fuelcells, also known as PEM fuel cells. Polymer electrolyte membrane fuelcells are one type of proton exchange membrane fuel cells. Protonexchange membrane fuel cells have a separator or electrolyte between theanode and cathode that provides for transport of protons across theseparator. Generally, the separator/electrolyte is hydrated to performits function as electrolyte. The separator can be a polymer film. PEMfuel cells operate at lower temperatures than most other fuel cell typeswith operating temperatures generally less than about 100° C. and can beoperated at temperatures down to freezing.

Other types of fuel cells may also be appropriate, such as phosphoricacid fuel cells, molten carbonate fuel cells and solid oxide fuel cells.Phosphoric acid fuel cells use phosphoric acid as the electrolyte. Thesefuel cells generally operate at about 150° C. to about 220° C. Theelectrolyte for molten carbonate fuel cells is molten carbonate salts,as their name implies. To achieve sufficient ion mobility through thecarbonate salts, these fuel cells operate at temperatures on the orderof 650° C. The electrolyte for solid oxide fuel cells is a ceramic oxidematerial that can transport O₂ ions at temperatures from 600° C. toabout 1000° C. Phosphoric acid fuel cells, molten carbonate fuel cellsand solid oxide fuel cells are described, respectively, in U.S. Pat. No.5,302,471 to Ito et al., entitled “Compact Phosphoric Acid Fuel CellSystem And Operating Method Thereof,” U.S. Pat. No. 5,595,832 toTomimatsu et al., entitled “Molten Carbonate Fuel Cell,” and U.S. Pat.No. 5,595,833 to Gardner et al., entitled “Solid Oxide Fuel Cell Stack,”all of which are hereby incorporated by reference herein. Since PEM fuelcells are desired due to their operating temperatures and otherdesirable characteristics, the following discussion focuses on theseembodiments, although other fuel cell types can be substituted based onthe disclosure herein.

The fuel cell should have an appropriate response for a range of organicand/or inorganic analytes. While commercial fuel cells for ethanoldetection may not be well suited for the present applications, they mayprovide acceptable performance for the present applications. PEM fuelcells generally have catalyst materials in contact with both sides ofthe electrolyte/separator. One side forms the electrode exposed tooxygen in the atmosphere. The other side of the electrolyte/separatorforms the analyte electrode where the analyte reacts. Protons, or otheravailable ions, typically flow from the anode to the cathode as mediatedby the electrolyte.

In some embodiments, fuel cells with magnetic composites can beparticularly desirable due to their improved transport of paramagneticmaterials, such as oxygen to the appropriate electrode and enhanceelectrolysis. Fuel cells with magnetic materials incorporated into thefuel cell are described further, for example, in U.S. Pat. No. 6,479,176to Leddy et al, entitled “Gradient Interface Magnetic Composites AndMethods Therefor,” and U.S. Pat. No. 5,928,804 to Leddy et al., entitled“Fuel Cells Incorporating Magnetic Composites Having Distinct FluxProperties,” both of which are incorporated herein by reference.

For power production, fuel cells are generally formed into stacks with aseries of fuel cells connected in series to generate an additive voltagefrom the cells. Bipolar plates or other suitable current collector withflow channels separate adjacent cells. However, in general, for thepresent application, a single fuel cell of modest size is suitable thatgenerates a reasonable voltage or current for the particular supply ofvolatile analytes. Voltage is not substantially dependent on electrodearea, although small changes in internal resistance may relate toelectrode area. Using a single fuel cell, the structure of the systemscan be much simpler in comparison with a fuel cell stack, especiallywith respect to the flow of analyte and counter electrode reactant,generally, oxygen from air. However, the sensor can use a plurality offuel cells, such as two fuel cells or more than two fuel cells,connected either in parallel of in series to obtain desiredresponsiveness of the sensor.

The analysis is based on a unique time dependent signature of thedifferent volatile organic and/or inorganic compositions with respect tothe time dependent response of a fuel cell operating using the volatileorganic composition as a reactant. To de-convolute the time dependentresponse curve, standard curves are generated for selected volatilereactant compounds or particular mixtures thereof, which are thought orknown to be in a breath sample for analysis. The particular mixtures canbe analyzed together as a particular composition. For example, tobaccosmoke, such as cigarette smoke, has a mixture of volatile organiccompounds that are relatively fixed with respect to relative amountssuch that the mixture can be considered a separate composition that isanalyzed together for the purposes of the de-convolution. The standardcurves can be based on averages from a plurality of runs to improve theprecision and accuracy of the standard curve. Then, the sample curve canbe de-convoluted with the standard curves. The de-convolution can bebased on a linear or non-linear combination at a plurality of timepoints.

The methodologies described herein can be used in a variety ofapplications, such as breathalyzers, vehicle interlocks, medicaldiagnostics, screening of large populations and environmentalevaluations. Fuel cells are already used commercially for breathalyzersfor the detection of ethanol to determine if the values are within legallimits. Portable devices can be used by law enforcement officials fortesting drivers suspected of driving under the influence of alcohol.Similarly, other devices have been connected to vehicles, especiallyautomobiles, for the evaluation of the sobriety of a potential driverand disabling the vehicle as appropriate. These devices can benefit fromthe improved analytical systems and methodologies described herein sincemore accurate readings can be obtained if a variety of sources ofvolatile organic compositions can be distinguished. Present commercialfuel cell breathalizers generally are not suitable alone for evidentiarypurposes.

Furthermore, volatile compositions, e.g., organic solvents, are oftenenvironmental pollutants that result from a wide range of humanactivities. The ability to efficiently identify pollutants in aparticular gas sample can greatly facilitate the evaluation of apotential environmental pollutant. Similar to the evaluation ofenvironmental pollutants, analyses can be performed in industrialsettings to evaluate release of pollutants and/or to evaluate exposurelevels to individuals to determine if they are within acceptable levels.These industrial limits may be evaluated in view of specificregulations, such as regulations from the U.S. Occupational Health andSafety Administration (OSHA) or the U.S. Environmental Protection Agency(EPA).

In other embodiments, measurements from the systems described herein canassist with medical evaluations since the presence of certaincompositions in the breath can be indicative of certain illnesses orconditions. For example, the level of acetone in a person's breath canbe used to evaluate the presence of a diabetic condition or similarly toevaluate the maintenance of the person's diabetic control. As seenbelow, acetone has a distinctive signature in the fuel cell responsecurve that can be used to evaluate acetone concentration. In otherembodiments, the analyzer can be used to evaluate the health of aperson, who may or may not have identifiable symptoms. For example, theanalysis can be performed as part of a well patient visit for the earlydetection of conditions such as diabetes. Alternatively, the analysiscan be performed as part of a diagnosis procedure on a patient withsymptoms that have not yet been definitively connected with a particulardisease. For the measurement of unknown analytes for medical diagnosis,the fuel cell response curve is generally deconvoluted with respect to arange of potential analytes found in a person's breath. For samplesthought to include acetone, the distinctive negative peak can be usedfor evaluating the concentration of acetone, and several specificalgorithms are described below.

Once estimates of the concentration of medically related analytes aredetermined within a person's breath, these breath concentrations can becorrelated with serum concentrations using either known relationships orrelationships that can be determined through measurements on individualswithin known medical conditions. Using the estimates of serumconcentrations of various analytes, this information can be incorporatedas additional data that can be used for diagnostic purposes along withother tests and examinations. Generally, a medical professional would beinvolved in the evaluation of the collective test information forarriving at the ultimate diagnosis.

Vapor Analysis System

A vapor analysis system generally comprises a vapor/gas samplingcomponent, a flow apparatus, a fuel cell and an analysis instrument. Thesampling component can be designed based on the source of the particularsample. In some embodiments of interest, the vapor sampling componentcan be a breath collection component. The flow apparatus provides forcontrolled flow of the vapor sample to the fuel cell. The analysisinstrument collects the time dependent response of the fuel cellfollowing interaction with the vapor sample and the de-convolution ofthe time dependent response of the fuel cell to obtain the relativeamounts of the samples. While the fuel cell can be optimized for certainanalytes such as ethanol, general fuel cells can be used that areresponsive to organic compositions and/or inorganic compositionsgenerally. Thus, a fuel cell sensor may or may not be sensitive to aparticular analyte depending on the particular objective of the device.

Referring to FIG. 2, a schematic diagram is depicted for a vaporanalysis system described herein. A vapor analysis system 100 cancomprise sampling component 102, flow apparatus 104, fuel cell 106 andanalysis instrument 108. Sampling component 102 facilitates introducinga vapor sample into flow apparatus 104. In general, sampling component102 can be any mechanical or passive structure that facilitatescollection and introduction of desired vapors into the flow apparatusand/or the fuel cell of a vapor analysis system. Sampling component 102can comprise appropriate combinations of one or more tubes, mouthpieces,or the like.

In some embodiments, flow apparatus 104 can comprise inlet flow line110, which provides a fluid flow pathway for vapor samples from samplingcomponent 102 to the sample electrode of fuel cell 106. Flow apparatus104 can also comprise outlet flow line 112, which provides a vapor flowpathway for vapor samples and/or fuel cell by-products from the sampleelectrode of fuel cell 106 to, for example, exhaust 114. In someembodiments, flow apparatus 104 and/or fuel cell 106 can comprise one ormore pumps to facilitate moving vapor samples into and out of the sampleelectrode of fuel cell 106. Vapor analysis system 100 can be configuredto function in a variety of devices such as, for example, breathalyzers,ignition interlock systems, medical diagnostic devices, broad populationscreening, and environmental and industrial sensors or monitors.

As described below, the various components of vapor analysis system 100can be adjusted and designed to suit the intended application of thedevice. In embodiments where the vapor analysis system is designed to beincorporated into a breathalyzer, sampling component 102 can comprisestem having a tube fitting adapted to removably engage a sample tube ormouthpiece. In some embodiments, the stem can be formed integrally withthe housing of the breathalyzer. Breathalyzers having a stem and a tubefitting are described in U.S. Pat. No. 4,487,055 to Wolf, entitled“Breath Alcohol Testing Device,” which is hereby incorporated byreference herein. In embodiments where the vapor analysis system isdesigned to be incorporated into an ignition interlock, samplingcomponent 102 can comprise a mouthpiece that extends from the interiorof the housing to the exterior of the housing. Sampling components forignition interlocks are described in, for example, U.S. Pat. No.5,426,415 to Prachar et al., entitled “Breath Analyzer For Use InAutomobile Ignition Locking Systems,” which is hereby incorporated byreference herein. In other embodiments, the vapor analysis system can beincorporated in medical diagnostic devices or an environmental sensor ordetector. Suitable breath sampling components for a medical examinationare described, for example, in U.S. Pat. No. 5,081,871 to Glazer,entitled “Breath Sampler,” incorporated herein by reference. Suitableenvironmental sampling systems are described for example in U.S. Pat.No. 5,753,185 to Mathews et al., entitled “Vehicle Emissions TestingSystem,” incorporated herein by reference.

The vapor analysis devices 100 of the present disclosure can comprise aflow apparatus 104 that provides desired fluid flow within vaporanalysis device. In general, flow apparatus 104 can regulate and providefluid flow to and from fuel cell 106 during analysis of a sample. Flowapparatus can comprise appropriate combinations of flow lines or pipesand one or more pumps to facilitate desired fluid flow within vaporanalysis system 100. The pump and/or other flow control elements can beconnected to a microcomputer, which can control the function of thepump, and thus the introduction of vapor samples into fuel cell 106.

A flow apparatus suitable for use in ignition interlock systems isdisclosed in, for example, U.S. Pat. No. 5,426,415 to Prachar et al.,entitled “Breath Analyzer For Use In Automobile Ignition LockingSystems,” which is hereby incorporated by reference herein. In thissystem, a diaphragm pump is used to divert a portion of a breath samplethrough a fuel cell sample electrode while exhausting flow from thesample electrode. Flow structures suitable for use in a breathalyzersare described in, for example, U.S. Pat. No. 4,487,055 to Wolf, entitled“Breath Alcohol Testing device,” and in U.S. Pat. No. 5,291,898 to Wolf,entitled “Breath Alcohol Device,” both of which are incorporated hereinby reference. In these systems, a diaphragm draws breath into and from achamber adjacent a fuel cell sample electrode to control exposure of thefuel cell sample electrode to the breath.

As described above, flow apparatus 104 can be connected to one or morefuel cells 106 to facilitate analysis of a vapor sample. Fuel cell 106can be any fuel cell that can produce a response to desiredcompositions. Suitable fuel cells include, for example, PEM fuel cells,phosphoric acid fuel cells, molten carbonate fuel cells and solid oxidefuel cells, as noted above. In some embodiments, fuel cell 106 can be aPEM fuel cell comprising a proton exchange membrane as the electrolyte,such as Nafion®, with catalyst particles in contact with the electrolyteforming the sample electrode and counter electrode. A current collectorcontacts the electrolyte particles to complete the electrodes. Aparticular embodiment is described further with respect to the Examplesbelow. In some breathalyzer fuel cells, the separator is formed fromsintered or pressed polymer balls, such as polyvinylchloride, to formpores with about 1 to about 25 micron diameters extending through themembrane. A layer of catalyst mixed with conductive carbon and binder isapplied to each side of the membrane to form the sample electrode andthe counter electrode. The porous framework can be filled with sulfuricacid, phosphoric acid or a mixture thereof to complete the circuit,although other electrolytes can be effectively used.

Suitable analysis instruments include, for example, windows basedcomputers, person digital assistants, and dedicated computer processors,i.e., microprocessor, integrated into a portable analysis apparatus, inwhich portable digital assistant technology can be incorporated into theapparatus.

Acetone Detection

As will be discussed below, the time response to acetone produces asignature negative curvature when using a fuel cell sensor. This timeresponse indicates that the acetone is reduced at the fuel cell cathode,although the reduction product may be subsequently oxidized at the sameelectrode. After an initial rise, the signal from an aqueous acetonesolution rapidly decreases and then increases to a maximum followed byslow decay. This distinctive short time negative peak from acetonereduction can provide a signal that enables ready estimation of acetoneconcentrations in alveolar air and discriminate against possibleinterferents on the breath, such as ethanol and cigarette smoke. Thedistinctive wave shape for acetone is observable at suitably lowerconcentrations of acetone of biological relevance. Thus, a breath-basedfuel cell sensor enables quantifying breath acetone that is associatedwith diabetic ketoacidosis and effective management of a ketogenic dietand serves as a screening tool for the diabetic state.

Due to the presence of the negative peak, several approaches can be usedto estimate the amount of acetone in a person's breath sample. However,the acetone signal with the negative peak is generally on the same orderof magnitude as a “clean” breath signal (breath with no detectableacetone concentration). In contrast, the response of the fuel cellsensor to ethanol and smoke is much stronger than fuel cell response to“clean” breath (or acetone spiked breath) so that a “clean” breathsignal generally can be ignored for an ethanol evaluation. In otherwords, any signal due to “clean” breath does not need to be subtractedfrom the representative ethanol or smoke signals, and a “clean” breathsignal does not need to be subtracted from actual breath samples toevaluate significant ethanol contributions. On the other hand, todetermine the presence and amount of acetone on a person's breath, asubject's “clean” breath generally may be taken into consideration insome analysis approaches.

Three different algorithms to estimate the acetone concentration in aperson breath are discussed. In the following section, an approach forevaluating a person's breath for a plurality of medically relatedanalytes is discussed. The concentration of acetone in the person'sbreath provides information on the corresponding blood sugar level(s)and metabolic/diabetic state of the subject. The potential or current isa function of time, and these functions can be considered vectors from acalculational perspective with the discrete time points selected asdescribed below in the context of more general algorithms for multipleanalytes.

In a first approach, the fuel cell sensor signal is taken as a linearcombination of an acetone signal and a “clean” breath signal. To build acalibration set, a signal vector can be used that has a known amount ofacetone (V_(acetone), which is an acetone concentration slightly abovethe highest level one would ever expect to find on a subject's breath).A signal that is known to not carry acetone (V_(clean)) is subtractedfrom V_(acetone). “Clean” samples can be collected by bubbling clean,compressed air or a healthy person's breath through a water bath at 37°C. (body temperature). Linear combinations Of V_(acetone) and V_(clean)are taken to match a real subject's breath (V_(sample)) and anappropriate calibration table based on the original concentration ofacetone used to collect V_(acetone) is used to determine the acetoneconcentration. To evaluate the amount of acetone in an individual'sbreath, the following equations can be used with a non-linear fit usingthe vectors:

C ₁×V_(acetone) +C ₂×V_(clean=V) _(lin. Combo),  (1)

Absolute Value of Σ(V_(lin. Combo)−V_(sample))_(i)=Gross Error,  (2)

where the minimum Gross Error (GE) gives the best fit result. Thesummation for calculating the Gross Error involves a summation overdifferent time points. To do the minimization, the values of C₁ and C₂can be obtained by minimization for each concentration dependentstandard vector V_(acetone)(C_(a)) where C_(a) is a particular acetoneconcentration for evaluating the standard potential or current responsecurve. Then, for each value of the standard concentration, the grosserror can be evaluated. The sample concentration can be estimated as theconcentration of the standard response curve that leads to the lowestgross error.

Since the most distinctive portion of the acetone response curve is atrelatively short times, the calculation can be weighted using a fixedweight vector, V_(weight). For example, the weight vector can have astep function that cuts off the time at a cut off, such as 35 seconds.As another example, V_(weight) can be two from 5 to 15 seconds and oneelsewhere. Then the gross error can be generalized to the following:

Absolute Value of Σ(V_(weight)·(V_(lin. Combo)−V_(sample))_(i))=GrossError,  (3)

which involves a vector dot product and i indicates a particular timepoint.

By developing the table of linear combinations of V_(acetone) andV_(clean) ahead of time, a very simple instruction set can be used thatwould execute quickly on even the very low cost hardware presentlyavailable. If the negative acetone spike is not linear with theconcentration of acetone the subject's breath is compared with a tableof vectors; each vector representing a particular acetone concentration.These vectors can be determined experimentally using the bubblingapparatus described in the Examples below.

In a second approach, the magnitude, i.e., depth, of the negative peakis used directly to estimate the acetone concentration. The negativepeak is superimposed on a positive slope from “clean” breath andpossibly an oxidation response related to the oxidation of the acetonereduction product. The negative peak depth can be estimated from theposition of the negative peak subtracted from an estimate of thepositive going contribution. The positive going contribution can beestimated with a line connecting the local maxima on either side of thenegative peak.

In principle, the first derivative curve can be used to locate the localextrema, but this can be complicated from noise in the plot that masksthe desired local extrema by generating a large number of local maximaand minima. A variety of numerical approaches can be used to identifythe desired extrema, which may or may not involve the input regardingwindows on the expected location of the extrema. In a representativeapproach, the end points of the acetone peak are located with a stepwiseapproach to identify the local extrema, i.e., a local maximum or a localminimum. The extrema, involving two local maximum with a local minimumbetween them, can be located as time progresses. In particular, thefirst maxima V_(M1) can be picked as the highest value from an initialtime before a significant dip. The negative peak or dip can beidentified as a drop of at least about 1% in magnitude from the firstmaximum. The lowest point V_(min) in the dip can be identified when thecurve increases by at least about 1% in magnitude from the previousminimum. Also, the second maximum can be identified by a subsequent dropof 1% in magnitude from the previous maximum.

More specifically, in one embodiment, starting from an initial reading,a tentative maximum potential/current reading and its corresponding timevalue are stored as one progresses in time with a new maximum replacinga previous tenetative maximum until a present potential/current readinghas dropped at least 1% from the previous maximum reading. When the 1%drop is identified, the tentative maximum potential/current reading isthen saved with its time as V_(M1), t_(M1), respectively. Then, a goingforward tentative minimum and its corresponding time are stored with anew minimum replacing a previous tentative minimum until thepotential/current has gone back up by at least about 1% from theprevious tentative minimum value. When this point is reached, thetentative minimum potential/current value is stored along with its timeas V_(min), t_(min) respectively. Then, a forward going tentativemaximum value and corresponding time are stored with a new maximum valuereplacing a previous tentative maximum value until the currentpotential/current value has fallen by at least 1% from the previoustentative maximum value. When this point is reaches, the tentativemaximum value and corresponding time are stored as V_(M2), t_(M2),respectively.

Once these values are obtained, a line is fit through (V_(M1),t_(M1))−(V_(M2), t_(M2)) by solving linear equations for m and b:

V_(M1) =m×t_(M1) +b,

V=m×t_(M2) +b.

Then, the depth D of the negative peak is evaluated asD=V_(min)−(m×t_(min)+b). To obtain the concentration from this value forD, a set of standard values D_(s) can be evaluated for D using standardacetone solutions at 37° C., D_(s)(C_(i)), where C_(i) are a set ofstandard concentrations. Either the value of C_(i) corresponding toD_(s)(C_(i)), which is the closest value of D_(s) to the sample value ofD, can be selected as the estimate of the acetone concentration in theperson, or a linear or nonlinear extrapolation can be performed betweenthe two closest values of D_(s) to get a more precise value of acetoneconcentration if a higher precision is desired.

In a third approach, the area of the negative spike can be evaluated toestimate the acetone calculation. While slightly more involved than thenegative peak depth measurement, the area based approach on averageshould be less sensitive to noise so that it may be slightly moreaccurate than the peak depth approach. To estimate the negative peakarea, the values of t_(M1) and t_(M2) can be used to fix the end pointsof the peak. The difference is evaluated between the line V₁=mt+b, withm and b determined as described above and V_(i) where this is aparticular potential/current reading along the negative peak. Thedifference between the sample value V_(i) and linear V₁, i.e., (Vi−V₁),is integrated between the two time values, t_(M1) and t_(M2). The areais the integral of this difference, and the integration can be performedwith any standard numerical integration routine, such as the trapezoidrule integration or others known in the art.

The evaluated area can be compared with standard areas evaluated withstandard aqueous acetone solutions. Again, the concentration can beestimated by finding the concentration C_(i) corresponding with theclosest standard area A_(i) to the sample area A. If greater precisionis desired, the standard area values can be linearly interpolated toobtain a more accurate estimate of the sample concentration.

Analysis Algorithm

The time dependent response of the fuel cell is dependent on thechemical composition of the sample introduced into a vapor samplingsystem of the fuel cell sensor, such as those described herein. Thus, ifa sample comprises a plurality of volatile organic and/or inorganiccompositions that can react at the fuel cell electrode, the timedependent response curve of the fuel cell reflects the overallcomposition of the vapor sample. The de-convolution of the timedependent response curve can be used then to obtain the amounts of thedifferent volatile organic compositions in the vapor sample. Thede-convolution can be based on a linear combination or a nonlinearcombination of the independent response curves. The de-convolution ofthe vapor sample is based on standard curves for the individualcompositions, which may be normalized.

The use of a fuel cell signal to de-convolute ethanol contributions fromcigarette smoke contributions is described further in published U.S.Patent application 2005/0214169A to Leddy et al., entitled“Multicomponent Analysis of Volatile Organic Compositions in VaporSamples,” incorporated herein by reference. Here, the analysis isgeneralized to provide for medical diagnosis assisted with an analysisof a breath sample. Acetone is a significant composition for a medicalevaluation since it is indicative of diabetic individuals and theirmaintenance of their condition. As noted above, acetone has acharacteristic negative peak at relatively short times indicating areduction reaction.

The procedure for medical evaluation generally involves a significantcomponent related to the selection of analytes for the de-convolution ofthe potential/current measurements. To be applicable for a large numberof individuals, it is useful to include cigarette smoke and ethanolsince these compositions may be on the breath of individuals beingevaluated for medical conditions and since these compositions generallyyield relatively strong signals. Other analytes of interest include, forexample, CO₂, COS, NH₃, CS₂, alkanes, benzene derivatives and NO. Ingeneral, the analytes for these measurements may not yield signals asstrong as an ethanol signal of an intoxicated person or cigarette smokefor a person who has smoked shortly before the measurement. For thesesamples, the background “clean” breath signal may be relevant. “Clean”samples can be collected by bubbling clean, compressed air or a healthyperson's breath through a water bath at 37° C. (body temperature). Then,the clean breath measurements can be included as one of the analyteswithin the de-convolution of the sample measurement curve. Thede-convolution should work even if an analyte has a negative/reductionpeak in its response curve, such as acetone.

To perform the analysis, each curve can be converted to a vector by theselection of a specific number of time points. The dimension of eachvector, i.e., the number of time points used, can be selected to obtaina desired degree of fitting. The number of time points is selected toyield a desired accuracy of the de-convolution. All of the collectedtime points can be used in the analysis such that the hardware responsetime sets the spacing of the time points, although a subset of the timepoints can be used as desired. Generally, the data are collected untilthe signal has significantly decayed from its peak value, and in someembodiments the signal is monitored until it has decayed 60 percent fromits peak value, in further embodiments 75 percent from its peak valueand in additional embodiments 85 percent from its peak value. A personof ordinary skill in the art will recognize that additional ranges forthe time cut-off within the explicit ranges are contemplated and arewithin the present disclosure.

Generally, the degree of fitting does not significantly increase after acertain number of time points are selected. The number of time pointsmay be fixed by the timing of the data collection system and theresponse time for the analog-to-digital conversion. In general, the timepoints do not necessarily have to be equally spaced, although certainspacings may be convenient for certain types of numerical analysis. Theresulting vector can be written as V with elements v_(n) for the nthtime point recorded.

Each standard vector can normalized to a normalized vector NV for thelater de-convolution. Specifically, the normalization is performedaccording to:

The Nth Normalized element inNV=Nv_(n)=(v_(n)−v_(small))/(v_(large)−v_(small)),  (1)

where v_(small) is the smallest element in V, v_(large) is the largestelement in V. Equation (1) ensures that the largest value in NV is 1 andthe smallest value is 0. Other normalizations can be used to standardizethe peak value, if desired, although the normalization in Eq. (1) hasbeen found in the examples below to yield good results.

A number of normalized curves from known samples can be averaged to geta standard curve V_(average) for a particular analyte, such as acetone,ethanol, cigarette smoke, COS, CS₂, NO, clean breath or any othervolatile organic and/or inorganic composition. Depending on themagnitude of the other signals in the sensor, it may be useful toinclude “clean” breath as one of the analytes. The standard vector foranalyte “a” from an average of i sample runs can be written as:

Nth element of the standard vector for analyte a=V_(average, a=v)_(na)=(Nv_(na1)+Nv_(na2)+ . . . + NV_(nai))/i,  (2)

where Nv_(nai) is the nth normalized element at t_(n) for the i-thsample of analyte a. In general, slight variations between vectors NV₁to NV_(i) distort the values of V_(average), so that the largest valuein V_(average) is not necessarily equal to 1, and the smallest value inV_(average) is not necessarily equal to 0. Thus, the average responsecurve itself can be normalized based on the formula in Eq. 1 to obtain anormalized average or standard curve for a particular analyte,NV_(average).

A linear combination of the vectors NV_(average,A), NV_(average,B),NV_(average,C), etc. can be used to form a vector V that approximates asample vector. In principle, any number of standard vectors for analytesA, B, C, D, . . . can be used. In some embodiments, there are 2analytes, such as acetone and cigarette smoke, in other embodiments, 3analytes, in further embodiments 4, in additional embodiments 50 ormore, and any number in between. The instrument can use alternativealgorithms based on input from the operator. For example, an algorithmcan be selected that includes de-convolution involving cigarette smokeif the subject is a smoker or ethanol if the person had a drink in theprevious 6 hours or other time threshold. A selection among a fewdifferent algorithms can provide improved sensitivity for otheranalytes, and the instrument generally can be simply programmed toachieve this selection among algorithms.

For two analytes, the equation is as follows:

V_(Lin. Combo.)=A×NV_(average,A)+(1−A)×NV _(average, B), where0<=A<=1  (3).

For three analytes A, B and C, this equation becomes:

V_(Lin. Combo.)=A×NV_(average,A)+B×NV_(average,B)+(1−A−B)×NV_(average,C),  (4)

where 0<=A<=1 and 0<=B<=1. Equations for other numbers of analytes canbe written based on these examples. In general, there are N−1 unknownsfor N analytes. Thus, as long as there are at least N−1 time points, thelinear combination (or nonlinear combination) can be fit, althoughhaving additional time points presumably leads to a better fit throughan over determination of the linear fit.

The new linear combination vector can be normalized according to Eq. 1.Similarly, the sample vector can also be normalized to yield a vectorNV_(unknown). Because the two vectors, NV_(Lin.Combo). and NV_(unknown)are normalized to the same range, the proportions of the signal due tothe two analytes, ethanol and cigarette smoke in the example below, theproportions of the two analytes can be evaluated regardless of theabsolute magnitude of the response. The calculation at some pointinvolves scaling the linear combination curve to the actual measurementto obtain the absolute quantities of the analyte. This scaling back tothe total values can be performed before or after the fitting.

The best fit for the unknowns can be determined using establishedmathematical techniques. Thus, for Eq.3, A_(Best Fit) is determined, andsimilarly, for Eq. 4, A_(Best Fit) and B_(Best Fit) can be determined.For example, the unknown parameters can be obtained by iteration. Thesum of the differences between the elements in NV_(Lin. Combo). andNV_(Unknown) can be called the “Gross Error” and is defined by thefollowing equation:

Gross Error=Σ|Nv_(n,unknown)−Nv_(n,Lin. Combo.)|  (5).

Equation (5) results in a fit that weights all time points equally.Other expressions for the gross error can be used, if desired. Thisfitting to reduce the gross error to obtain the best fit can usestandard approaches to automate the process. An initial value can beestimated for the parameters based on known information about thesample. Standard methods for performing the fit are known, such as theDownhill Simplex Method and the Conjugate Gradient Method. These aredescribed further, for example, in Numerical Recipes: The Art ofScientific Computing, W. H. Press et al., (Cambridge University Press,1986), incorporated herein by reference.

Once the value of A_(BestFit) is known, it can be used to obtain BrAC orother concentrations for other analytes besides ethanol. Similarly, ifadditional unknown parameters are calculated for other analytes, thesecan be used to obtain useful concentration information. For a particularparameter, the concentration data can be obtained from the followingcalculation:

BrAC or other Concentration value=A _(BestFit) ×C_(Calibration)×(V_(large,LinCombo)−v_(small,LinCombo))×(v_(large,Unknown)−v_(small,Unknown))  (6).

Similar integration based calibrations to obtain areas of peaks or otherareas of the time response curve are also possible to obtainconcentrations.

C_(calibration) can be obtained from a calculation of the responses of afuel cell to a known, pure analyte sample. The concentration value forthe vapor sample is then divided by the response to yieldC_(Calibration). The last part of Eq. (6), i.e.,(v_(large,LinCombo)−v_(small,LinCombo))×(v_(large,Unknown)−v_(small,Unknown)),uses the largest and smallest values in vectors V_(Unknown) andV_(LinCombo) and is a scalar ratio between the range of the response tothe unknown sample and the range of the linear combination fit. Sincethe linear combination of the normalized analytes responses are used,this scalar ratio can be used to find the actual response curve for thatanalyte.

Equations (3) and (4) above are directed to linear combinations of thefuel cell response for the different analytes. However, there may becircumstances in which the analytes interact in the anode such that theresponse of the fuel cell may be non-linear with respect to the presenceof the different analytes. For example, Eq. (3) can be generalized to:

V_(Nlin. Combo)=A×NV_(average,A)+(1−A)×NV_(average,B)+C×(NV_(average,A)×NV_(average,B)).  (7)

The parameters for the nonlinear fit can be established by obtaining thesmallest value of the Gross Error in a similar fashion as the linearparameters were established. The parameters A and (1-A) can similarly beused to evaluate concentrations of the analytes as described above.

EXAMPLES

In performing the measurements herein, the apparatus incorporated adesign with a fuel cell as described below. The fuel cell is placedwithin the apparatus to provide regulated breath flow to the anode ofthe fuel cell. The fuel cell output voltage was converted to a digitalsignal with an A/D converter for analysis by a computer. The analysiswas performed through an iteration using 0.001 increments in theparameters over the range of possible values. This approach isstraightforward to implement with computationally limited processors.

The fuel cells used in the test devices are essentially described inU.S. Pat. No. 5,928,804 to Leddy et al., entitled, “Fuel CellsIncorporating Magnetic Composites Having Distinct Flux Properties,”which is hereby incorporated by reference, except that the fuel cellsdid not contain magnetic composites. These fuel cells are protonexchange membrane fuel cells with Nafion® perfluoronated, sulfonic acidpolymer used as the electrolyte/separator. The ionomer Nafion® hassuperior ionic conductivity. Platinum coated carbon black particles (20weight percent platinum) were used as the catalysts. The catalystparticles are formed by mixing Pt from Alfa Aesar with carbon black(XC-72 from E-Tek) and mixing vigorously with a drill. The fuel cellswere prepared with catalytic ink preparation and application procedures,Nafion® membrane pretreatments, and hot press lamination techniques.Specifically, a catalytic ink is mixed from platinum, carbon black,water, ethanol and isopropyl alcohol. This combination is mixedthoroughly. This solution is applied to carbon cloth or carbon paper(Toray paper) by painting with a brush or spraying with an air brush.Suitable carbon paper or carbon cloth are available from AldrichChemical or E-Tek. Solubilized Nafion® (Ion Power or Aldrich) is thensprayed over the dry ink that has already been supplied to theelectrode(s). The two counter electrodes are formed equivalently. ANafion® membrane (Aldrich or Ion Power) is sandwiched between the twoelectrodes and hot pressed at about 130 degrees C. under about 0.1metric tons per square centimeter. The membrane electrode assembly (MEA)is allowed to cool while under pressure. Once cooled to about 50 degreesC., the MEA can be removed from the press for use.

The catalyst/separator interface has the bulk of the catalyst sitesavailable to volatile reactive compositions in the vapor sample, incomparison with commercial breathalyzer fuel cells. The fuel cells werecircular with diameters of about 1.5 centimeters and are mounted incartridges for easy exchange within the testing apparatus. Due to theirconstruction and corresponding high general sensitivity, the fuel cellshave unique time response curves with respect to volatile compositionsof interest. In particular, commercial breathalyzer fuel cells tend tobe much more sensitive to ethanol than to other types of volatileorganics.

The test apparatus provided for the introduction of various breathsamples under specified conditions into the fuel cell. Data werecollected at 10 Hz, i.e., 10 points per second for 1 to 100 seconds fora total of 990 time points. For the examples below directed to theseparation of measurements of ethanol and cigarette smoke, the cut offfor the time was not significant after 30 seconds. Some testing wasperformed with standard solutions, while other testing was performedwith actual human breath samples. To produce a breath-based test sample,human subjects blew into the device for ten seconds. The first sixseconds of the sample were allowed to bypass the fuel cell. The lastfour seconds of a run come from deeper within the subject's lungs. Thus,last four seconds of the breath is sampled for flow into the fuel cell.A pump with a valve system controls the flow to the fuel cell. Acomputer records the fuel cell voltage multiplied by 10,000 as afunction of time. The fuel cell temperature was 25° C. for all samples.To control the conditions for the test, the tests were performing a in acontrolled environment chamber.

Example 1 Evaluation of Reproducibility

This example is directed to the evaluation of reproducibility of thetime response curves for different fuel cells assembled as describedherein.

Three fuel cell test devices were constructed as described above.“Breath” samples were produced and introduced into each of the fuel celltest devices. The “breath” samples were generated using a ToxitestBreath Alcohol Simulator containing a 0.05 BAC standard solution. Thesolution was formed with a mixture of ethanol and water to simulate ablood sample and heated to 37° C. to simulate body temperature. Breathwas bubbled through the solution to simulate a breath sample. A knownquantity of the “breath” sample was introduced into the fuel cell ofeach fuel cell test device.

As depicted in FIG. 3, the shape of the amplified potential v. timecurve is similar for all three fuel cells. However, the magnitude of thethree curves are different, with fuel cell 1 having the largestmagnitude, followed by fuel cell 2 and fuel cell 3, respectively. Themagnitude of each curve is different because of the inexact nature ofcatalyst application during construction. In other words, one particularfuel cell may have more catalyst than another fuel cell, which appearsto affect the magnitude of the response curve but not the general shapeof the response curve. Since the shapes of the curves are the same, theusefulness of the fuel cells for the detection of different volatileorganic compositions should not depend on the magnitude of the availablecatalyst and differences in catalyst loading is at least in partcorrected by the normalization.

Example 2 Cigarette Smoke Detection

This example is directed to detection of cigarette smoke in breath as afunction of time from inhaling smoke from the cigarette.

Breath samples containing cigarette smoke from a single individual wereintroduced into a fuel cell test device as described above. The shape ofthe amplified potential v. time curve for each breath sample wassimilar, however, the magnitude of the curve decreased over time as thenumber of “clean” breaths after inhalation of the cigarette increased.In other words, the magnitude of the response to cigarette smoke isdependent on time. Referring to FIG. 4, the curve with the largestmagnitude was from a breath sample taken after no clean breaths, whilethe curve with the smallest magnitude was from a breath sample takenafter two clean breaths after a drag. The shape of the amplifiedpotential v. time curve has been found to be consistent for at least onehour after a cigarette has been inhaled. Additionally, the shape of thecurve has been consistent for different brands of cigarettes. Thus, theapproaches described herein can be generally effective for the detectionof cigarette smoke on human breath.

Example 3 “Clean” Breath Samples

This example is directed to the comparison of the breath of a non-smokerwith the breath of a smoker after a 24 hour period without smoking.

Clean breath samples were introduced from different individuals into afuel cell test device as described above. Two of the individuals whoprovided breath samples were non-smokers, while the other individual wasa smoker who had not inhaled a cigarette for about 24 hours prior togiving the breath sample. As depicted in the FIG. 5, the shape of theamplified potential v. time curve for all three individuals is similar.Thus, significant voltage, or response, is not detected by individualswho have “clean” breath.

Example 4 Distinguishing Ethanol and Cigarette Smoke

This example demonstrates the ability to distinguish ethanol andcigarette smoke on the breath of a subject.

Breath samples from regular smokers who had been consuming alcohol wereintroduced into a fuel cell test device as described above. As depictedin the figures below, the total amplified potential v. time curve can beapproximated as a linear combination of the separate responses tocigarette smoke and ethanol components. based on the analysis usingEquations (3) and (5) above. The linear combination fit was conductedfor data taken between 1 and 100 seconds, using a data sampling rate of10 Hz.

Results from a first sample are depicted in FIG. 6. The curve wasde-convoluted to obtain the contributions from the cigarette smoke andthe ethanol, with the ethanol contribution having a larger magnitude andreaching a peak maximum at a significantly later time. A curve is alsoplotted of the ethanol and cigarette smoke linearly recombined. Thelinearly recombined curve is very close to the curve of an unknownsample. FIG. 7 is a graph of the absolute value of the normalizedresponse to the unknown sample minus the normalized linear combinationfit for the data depicted in FIG. 6. The sum of these errors yields thetotal gross error, which in this example, is 3.798. The time dependentresponse of the fuel cell from a second sample is depicted in FIG. 8along with the de-convoluted ethanol and cigarette smoke responses andthe linear fit curve. FIG. 9 is a graph of the absolute value of thenormalized response to the unknown sample minus the normalized linearcombination fit for the data depicted in FIG. 8. In this example, thegross error is 15.316. These are acceptable errors for this analysis.

This example illustrates that the fuel cell test device can be used toidentify multiple organic components in a sample. Additionally, the datapresented below in Table 2 represents results from 10 typical samples.The results were produced using the fuel cell device described above.The results in Table 2 indicate that the fuel cell test devices, alongwith the equations described above, can accurately determine the BrAC ofan individual who is consuming alcohol and smoking cigarettes. Thus, thefuel cell test devices can accurately determine the presence andrelative amounts of multiple organic compounds in a sample. Thecoefficient C in Table 2 is another notation for the parameter A fromEq. 3 for these particular analytes.

TABLE 2 Test Results of Ten (10) Typical Samples # Clean Breaths AfterCigarette Ethanol Consumed C BrAC from Fit GE Person X (Male) 0 2 oz. in~43 min. 0.117 0.05 12.564 1 2.5 oz. in ~57 min 0.299 0.06 6.022 2 3 oz.in ~73 min 0.468 0.06 3.831 4 3 oz. in ~96 min 0.632 0.07 5.771 >100(~10 min after last draw) 3 oz. in ~165 min 0.641 0.06 8.141 Person Y(Female) 0 1.5 oz. in ~29 min 0.130 0.05 10.063 1 2 oz. in ~48 min 0.5430.08 5.684 2 2.5 oz. in ~61 min 0.637 0.08 15.316 4 3 oz. in ~92 min0.683 0.08 3.798 >300 (~30 min after last draw) 3 oz. in ~160 min 0.7700.08 11.290

Example 5 Clean Breath Sample

This example demonstrates the fuel cell response to a “clean” breathsample from a healthy individual. In determining the presence and amountof acetone on a person's breath, a subject's “clean” breath generally istaken into consideration.

Referring to FIG. 10, the fuel cell response to three “clean” breathsamples from a healthy individual is depicted. The individual was atwenty-six year old male (non-smoker, had not been drinking) who hadeaten roughly two hours prior to providing the breath samples. The threeresponse curves have a similar shape to each other although they differin magnitude from each other.

Example 6 Acetone Measurements

This example is directed to the evaluation of the time response curvesfor different acetone concentrations.

A fuel cell was constructed as described above. The acetone samples weregenerated by bubbling air through an acetone solution heated to 37° C.to simulate body temperature using a concentration of acetone in thebath of 27 mM or 140 mM (1 mL and 5 mL of acetone diluted with 500 mL ofde-ionized water, respectively). The time response curves of the fuelcell are depicted for three separate samples at each of the twoconcentrations in FIG. 11 for 27 mM and in FIG. 12 for 140 mM. Thecurves indicate good reproducibility.

This example illustrates the unusual shape of the curve with thenegative component enabling identification of the acetone present in avapor sample. While the acetone signal is generally smaller with 27 mMacetone relative to the curve for 140 mM acetone, the signal is stillcharacteristic of acetone with the negative component to the response.The negative component indicates that the acetone is undergoing areduction reaction, although the resulting reduction product may besubsequently oxidized within the fuel cell. Nevertheless, thedistinctive negative peak can be used to analyze the breath of a personwith respect to detecting the presence and concentration of acetone in aperson's breath. The appropriate analytical techniques based on theseresults are presented above. In particular, average curves from theresults in FIGS. 11 and 12 can be used to obtain standard results forthese particular concentrations for use in the analyses above.

The embodiments above are intended to be illustrative and not limiting.Additional embodiments are within the claims. In addition, although thepresent invention has been described with reference to particularembodiments, those skilled in the art will recognize that changes can bemade in form and detail without departing from the spirit and scope ofthe invention. Any incorporation by reference of documents above islimited such that no subject matter is incorporated that is contrary tothe explicit disclosure herein.

1-8. (canceled)
 9. A method for diagnosing the health of an individual, the method comprising measuring the amount of a plurality of analytes in a breath sample from an individual by fitting a time response curve of a sample-evaluation fuel cell with at least one of the analytes having a negative potential peak in the time response curve indicating a reduction process, wherein a sample electrode of the fuel cell is exposed to a breath sample and the counter electrode of the fuel cell is exposed to a selected reactant.
 10. The method of claim 9 wherein an analyte having a negative potential peak in the time response curve is acetone.
 11. The method of claim 9 wherein the measuring is performed with a portable breathalyzer comprising the sample-evaluation fuel cell and a microprocessor.
 12. The method of claim 9 wherein the selected reactant comprises O₂. 13-26. (canceled) 